Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading

Ahuva Cern, Yechezkel Barenholz*, Alexander Tropsha, Amiram Goldblum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs' structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al., J. Control. Release 160 (2012) 147-157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-Nearest Neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used by us in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs.

Original languageAmerican English
Pages (from-to)125-131
Number of pages7
JournalJournal of Controlled Release
Issue number1
StatePublished - 2014

Bibliographical note

Funding Information:
We would like to thank the Barenholz lab members, with special thanks to Dr. Erez Koren, for their help in the experimental part of the work. AT appreciates the support from the NIH (grants GM66940 and GM 096967 ). The work was supported in part by the Barenholz Fund .


  • Iterative Stochastic Elimination
  • Liposomes
  • QSPR
  • Remote loading
  • Virtual screening
  • k-Nearest Neighbors


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